class_docstr: Example:
      >>> #The dataset is split into data, validation_data
      >>> model = ARIMAForecaster(p=2, q=2, seasonality_mode=False)
      >>> model.fit(data, validation_data)
      >>> predict_result = model.predict(horizon=24)
docstr: Evaluate using a trained forecaster.
  
  :param validation_data: A 1-D numpy array as the evaluation data
  :param metrics: A list contains metrics for test/valid data.
docstr_tok: ["Evaluate","using","a","trained","forecaster",".",":","param","validation_data",":","A","1-D","numpy","array","as","the","evaluation","data",":","param","metrics",":","A","list","contains","metrics","for","test\/valid","data","."]
code: def evaluate(self, validation_data, metrics=["mse"], rolling=False):
      """
      Evaluate using a trained forecaster.
  
      :param validation_data: A 1-D numpy array as the evaluation data
      :param metrics: A list contains metrics for test/valid data.
      """
      if validation_data is None:
          raise ValueError("Input invalid validation_data of None")
      if self.internal.model is None:
          raise RuntimeError(
              "You must call fit or restore first before calling evaluate!"
          )
      return self.internal.evaluate(
          validation_data, metrics=metrics, rolling=rolling
      )
  
file_path: analytics-zoo/pyzoo/zoo/chronos/forecaster/arima_forecaster.py
imports: from zoo.chronos.forecaster.abstract import Forecaster
  from zoo.chronos.model.arima import ARIMAModel
label_desc: Use image node_id 5 for calling the ARIMAForecaster obj's underlying member method code with example usage: obj.evaluate(validation_data, metrics, rolling) and returns: self